Systems and methods for waste item detection and recognition

ABSTRACT

Embodiments described herein relate to hardware and software for waste item detection and recognition, along with an education or feedback system. Embodiments described herein use artificial intelligence, which embodies machine learning and computer vision, to detect waste items and generate feedback to nudge the user to dispose the waste items into appropriate receptacles while generating smart operational insights of a designated premise.

FIELD

The present disclosure generally relates to the field of artificialintelligence and waste management. More particularly, the presentdisclosure relates to hardware and software for embedding artificialintelligence systems that utilize machine learning and computer visionto attempt to increase waste diversion rates.

INTRODUCTION

Landfills have placed an ominous effect on the environment andconsequently, governments have increasingly placed bans on landfillsthus making landfill-bound waste more expensive.

Many municipalities and other civic authorities have introducedrecycling programs to varying degrees. Waste items can be manuallycategorized and assigned to different receptacles for disposal accordingto their category.

However, currently available commercial technology does not fullyprovide a dynamic and automated approach to visually sorting the wasteitems in an individual's hands and nudge them into sorting physicallyinto the appropriate waste receptacles. Generally, systems rely on theusers/public to identify an appropriate waste category for each itemaccording to the local municipal recycling regulations and dispose ofitems accordingly. This is a highly cumbersome and ineffective processthat battles public apathy as well as confusion as recycling guidelinesare regularly updated, often legislatively with a promotional lag. Forexample, a user may be confused by labels on bins or items that areextremely simplified and do not provide recycling directions.Additionally, current recycling methods and categorization might not beintuitive. Users may not be able or willing to dispose of items in amanner desired by a local municipality or other authority. Recyclinglabels attempt to modify user behaviour without incentivising andengaging the user.

Measuring a facility's diversion or recycling efficiency can involve amanual waste audit. This localized method involves manual inspection ofselected bins within a facility to estimate diversion rates of thefacility. This method can be misleading, expensive, and time consumingbecause it only gives a mapped snapshot of a facility's waste intake forone single day. This method can require extrapolating any given days'worth of recycling to the entire year's waste patterns. The accuracy ofsuch a prediction is extremely low and ineffective in judging aproperty's waste data.

The audit method might not be used to estimate diversion rates unlessthere is an assumption that all waste items are being correctly disposedof; for example, that all items in a recyclable bin are in factrecyclable. This might not be the case. For example, a coffee cupincludes three items that belong in three distinct waste streams andthus should be sorted accordingly instead of dumped into a singlecontainer.

Waste audits by sorting bins might not be accurate due to the fact thateach individual sorting bin might have an isolated data set of wasteitems that was disposed within that particular bin. The datasets do nothave to be isolated in a bin scenario, they can be pushed onto multiplebins all across different locations if required via over the airupdates. A drawback with these however is that they do not educate thepublic, they are hardware heavy and hence not easy to implement at largescales which ultimately do not help with reducing contamination levelsat a higher level.

Embodiments described herein can enable real time waste auditing,computing metrics for prediction of fill levels using computer visionand machine learning of public facing waste receptacles using hardwareand/or software, such as sensor(s). Embodiments described herein canattempt to increase diversion rates within a facility and activelyeducates the public with feedback in order to create a more intuitiverecycling approach.

SUMMARY

In accordance with an aspect, there is provided a system for waste itemdetection. The system has sensors for detecting an approaching object totrigger a camera to capture image data and one or more processors toprocess the image data, wherein the sensors detect the approachingobject by computing a continuous decrease in signal range for theapproaching object. The system has a camera for capturing image data ofthe approaching object. The system has non-transitory computer readablestorage medium with executable instructions for causing the one or moreprocessors to: process the image data using a neural network to detect awaste item within the image data and determine a category for the wasteitem, wherein the neural network defines classes for different wasteitems and maps the detected waste item to the classes for the differentwaste items to compute a pairing of the detected waste item and a class,the class being associated with the category for the waste item;generate feedback data indicating the category for the waste item and anindication of an appropriate receptacle to dispose of the detected wasteitem. The system has a display device for displaying the feedback data.

In some embodiments, the processor is configured to use the neuralnetwork estimate a location of a head of the object within the imagedata and detect the waste item in the image data using the estimatedlocation of the head within the image data.

In some embodiments, the neural network is trained using image data todefine data points for person features, waste items, background andenvironment.

In some embodiments, the processor is configured to use the neuralnetwork to detect data points within the image data, the data pointscorresponding to a set of the person features, the set of the personfeatures defining head features for estimating the location of the headof the object.

In some embodiments, a portion of the data points corresponding to thebackground and the environment for filtering the image data to focus onthe detected waste item.

In some embodiments, the processor is configured to use the neuralnetwork estimate a location of hands within the image data based on thelocation of the head and detect the waste item in the image data usingthe estimated location of the hands within the image data.

In some embodiments, the feedback data indicates the location of theappropriate receptacle to dispose of the detected waste item.

In some embodiments, the neural network is trained through consecutiveobject detection to determine a correct receptacle that the waste itemshould be disposed.

In some embodiments, the camera and the sensors capture additional dataindicating disposal of the waste item, wherein the processor determinesan appropriate receptacle to dispose of the detected waste item, usesthe additional data to determine whether the waste item was disposed inthe appropriate receptacle, and generates additional feedback data basedon the determination, the processor configured to measure closeness of auser to the appropriate receptacle.

In some embodiments, the camera captures additional image dataindicating disposal of the waste item, wherein the processor determinesan appropriate receptacle to dispose of the detected waste item, usesthe additional image data to determine whether the waste item wasdisposed in the appropriate receptacle, and generates additionalfeedback data based on the determination.

In some embodiments, the display device displays the additional feedbackdata.

In some embodiments, upon determining that the waste item was disposedin the appropriate receptacle, the processor generates a reward forredemption as the additional feedback.

In some embodiments, the processor is configured to use the neuralnetwork estimate a location of hands within the image data and detectthe waste item in the image data using the estimated location of thehands within the image data.

In some embodiments, the neural network is trained using image data todefine data points for person features, waste items, background andenvironment.

In some embodiments, the processor is configured to use the neuralnetwork to detect person features within the image data, the personfeatures defining hand features for estimating the location of thehands.

In some embodiments, the processor is configured to use the neuralnetwork to detect person features within the image data, the personfeatures defining head features for estimating the location of thehands.

In some embodiments, the processor is configured to use the neuralnetwork to detect regions of the image data as background andenvironment and filter the regions of the image data to focus on thewaste item in the image data.

In some embodiments, the processor is configured to detect head datawithin the image data and blur the head data to generate sanitized imagedata.

In some embodiments, the neural network is trained using image data todefine data points for person features, wherein the person featuresdefine head features for detecting the head data within the image data.

In some embodiments, the processor is configured to tag the image datawith metadata indicating a system identifier, category for the wasteitem, location data, and time data.

In some embodiments, a cloud server is configured to receive the imagedata tagged with metadata indicating the category for the waste item.

In some embodiments, the cloud server is configured to process the imagedata to detect head data within the image data and blur the head data togenerate sanitized image data.

In some embodiments, a cloud server is configured to receive the imagedata tagged with metadata indicating the category for the waste item,validate the image data, generate a firmware upgrade for the neuralnetwork, and transmit the firmware upgrade to the processor to updatethe neural network.

In some embodiments, the processor is configured to compute imageanalytic data including types of waste items, volume of individual wasteitem, monitored volume of each receptacle based on the waste itemsdisposed, and calculated diversion rate.

In accordance with an aspect, there is provided a system for waste itemdetection. The system has sensors for detecting an approaching object totrigger image processing for detection and recognition. The system has acamera for capturing image data of the approaching object. The system asnon-transitory computer readable storage medium with executableinstructions for causing one or more processors to: process the imagedata using a neural network to detect a waste item within the image dataand determine a category for the waste item; generate feedback dataindicating the category for the waste item; and a display device fordisplaying the feedback data.

In some embodiments, the feedback data indicates an appropriatereceptacle to dispose of the detected waste item.

In some embodiments, the neural network is trained through consecutiveobject detection to determine where the waste items should be disposed.

In some embodiments, the camera captures additional image dataindicating disposal of the waste item, wherein the processor determinesan appropriate receptacle to dispose of the detected waste item, usesthe additional image data to determine whether the waste item wasdisposed in the appropriate receptacle, and generates additionalfeedback data based on the determination.

In some embodiments, the display device displays the additional feedbackdata.

In some embodiments, upon determining that the waste item was disposedin the appropriate receptacle, the processor generates a reward forredemption as the additional feedback.

In some embodiments, the processor is configured to estimate a locationof hands within the image data and distinguish the waste item in thehands within the image data.

In some embodiments, the processor is configured to detect head datawithin the image data and blur the head data to generate sanitized imagedata.

In some embodiments, the system has a cloud server configured to receivethe image data tagged with metadata indicating the category for thewaste item and process the image data to detect head data within theimage data and blur the head data to generate sanitized image data.

In some embodiments, the system has a cloud server configured to receivethe image data tagged with metadata indicating the category for thewaste item, validate the image data, generate a firmware upgrade for theneural network, and transmit the firmware upgrade to the processor toupdate the neural network.

In some embodiments, the processor is configured to compute imageanalytic data including types of waste items, volume of individual wasteitem, monitored volume of each receptacle based on the waste itemsdisposed, and calculated diversion rate.

In accordance with an aspect, there is provided a system for waste itemdetection. The system has a non-transitory computer readable storagemedium with executable instructions for causing one or more processorsto: process image data using a neural network to detect a waste itemwithin the image data and determine a category for the waste item, theimage data captured by a camera triggered by a sensors detecting anapproaching object; generate feedback data indicating the category forthe waste item; and a cloud server configured to receive the image datatagged with metadata indicating the category for the waste item andcompute image analytic data using the image data and metadata; aninterface for a display device for displaying the image analytic data.

In some embodiments, the processor is configured to detect head datawithin the image data and blur the head data to generate sanitized imagedata.

In some embodiments, the system has a cloud server configured to receivethe image data tagged with metadata indicating the category for thewaste item and process the image data to detect head data within theimage data and blur the head data to generate sanitized image data.

In accordance with an aspect, there is provided a system for waste itemdetection. The system has sensors for detecting an approaching object totrigger image processing for detection and recognition; a camera forcapturing image data of the approaching object; non-transitory computerreadable storage medium with executable instructions for causing one ormore processors to: process the image data using a neural network todetect a waste item within the image data and determine a category forthe waste item; generate feedback data indicating the category for thewaste item; and transmit the feedback data to a display device fordisplaying the feedback data.

In some embodiments, the feedback data indicates an appropriatereceptacle to dispose of the detected waste item.

In some embodiments, the neural network is trained through consecutiveobject detection to determine where the waste items should be disposed.

In some embodiments, the camera captures additional image dataindicating disposal of the waste item, wherein the processor determinesan appropriate receptacle to dispose of the detected waste item, usesthe additional image data to determine whether the waste item wasdisposed in the appropriate receptacle, and generates additionalfeedback data based on the determination.

In some embodiments, upon determining that the waste item was disposedin the appropriate receptacle, the processor generates a reward forredemption as the additional feedback.

In some embodiments, the processor is configured to estimate a locationof hands within the image data and distinguish the waste item in thehands within the image data.

In some embodiments, the system has a cloud server configured to receivethe image data tagged with metadata indicating the category for thewaste item, validate the image data, generate a firmware upgrade for theneural network, and transmit the firmware upgrade to the processor toupdate the neural network.

In some embodiments, the processor is configured to compute imageanalytic data including types of waste items, volume of individual wasteitem, monitored volume of each receptacle based on the waste itemsdisposed, and calculated diversion rate.

In various further aspects, the disclosure provides correspondingsystems and devices, and logic structures such as machine-executablecoded instruction sets for implementing such systems, devices, andmethods.

In this respect, before explaining at least one embodiment in detail, itis to be understood that the embodiments are not limited in applicationto the details of construction and to the arrangements of the componentsset forth in the following description or illustrated in the drawings.Also, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting.

Many further features and combinations thereof concerning embodimentsdescribed herein will appear to those skilled in the art following areading of the instant disclosure.

DESCRIPTION OF THE FIGURES

FIG. 1 is a top-down cross section view of a module according to anembodiment;

FIG. 2 is an overview of the placement of the module of FIG. 1 ;

FIG. 3 is an information flow diagram of the waste diversion systemprocess according to an embodiment

FIG. 4 is an exploded view of the module of FIG. 1 ;

FIG. 5 is open state view of the module of FIG. 1 ;

FIG. 6 is a diagram of a system for waste item detection and recognitionaccording to some embodiments;

FIG. 7 is a diagram of a system for waste item detection and recognitionaccording to some embodiments;

FIG. 8 depicts examples of augmented images;

FIG. 9 depicts example images of a class of waste item;

FIG. 10 is a front perspective view of a waste bin according to anotherembodiment;

FIG. 11 is an exploded view of the waste bin of FIG. 10 ;

FIG. 12 is a front perspective view a waste bin in an open stateaccording to an embodiment;

FIG. 13 is a flow chart of a waste item analysis process according to anembodiment;

FIG. 14 is a rear perspective view of a waste bin according to anembodiment;

FIG. 15 is a front view of a waste bin according to an embodiment;

FIG. 16 is a back view of the waste bin of FIG. 15 ;

FIG. 17 is a side view of the waste bin of FIG. 15 ;

FIG. 18 is a top view of the waste bin of FIG. 15 ;

FIG. 19 is a side view of a X-fin according to an embodiment; and

FIG. 20 is an end view of the X-fin of FIG. 19 .

Like reference numerals can indicate like or corresponding elements inthe drawings.

DETAILED DESCRIPTION

Embodiments of methods, systems, and apparatus are described throughreference to the drawings.

Embodiments described herein relate to artificial intelligence, and moreparticularly to a waste item recognition and recycling education systemutilizes machine learning and computer vision to detect waste items andnudge users into disposing them into the correct receptacle. The systemuses hardware and software for waste item detection and recognition andcan include an education or feedback system. The system uses machinelearning and computer vision to detect waste items and generate feedbackto nudge the user to dispose the waste items into appropriatereceptacles while generating smart operational insights of a designatedpremise.

Embodiments described herein relate to hardware and software forembedding artificial intelligence systems that utilize machine learningand computer vision to attempt to increase waste diversion rates. Theaforementioned system is an innovation in facility operationalefficiency and property waste management. Generating smart insights ofthe waste intake within a premise allows for optimal understanding ofthe waste patterns and consequent corrective actions by management.

Embodiments described herein can provide an artificial intelligencedriven waste recognition and recycling education system with objectdetection and recognition. The system can have a screen displayingfeedback indicating the appropriate receptacle for the user to disposethe detected waste item.

Embodiments described herein can provide an artificial intelligencedriven waste recognition and recycling education system. The system canhave a hardware and software module with sensors to detect approachingusers and trigger a vision system for object detection and recognition.The system can have a camera to capture images of the waste item for anevent. The system can be an embedded system which runs a predictionengine (e.g. deep learning process) that detects the waste item and thatcan train itself through consecutive object detection to determine wherethe waste items should be disposed. The system can have a screen todynamically provide feedback to nudge the user by either educating aboutcorrectly recycling the waste item in their hand, rewarding as theyrecycle properly or real time notifying when users have disposed thewaste item incorrectly using object tracking.

FIG. 1 is a top-down cross section view of a system 100 for waste itemdetection according to an embodiment. The system has a camera 104 forcapturing image data of waste items. The system 100 has two ultrasonicsensors 102 (in this example) that can trigger the camera 104 and thevision components to process the captured image data when it detects anobject (a person) in its array that is approaching a waste bin. Theimage data can be captured for an event. The event can be defined fromthe trigger (start) event to an end event (e.g. the person is walkingaway, the item is disposed of). The event can be linked to an eventidentifier for example. There may be more or fewer sensors 102 in otherexample embodiments. The vision components can be trained to focus onitems in the hands of the approaching person for further waste itemrecognition. Accordingly, a set of image data can be defined as an“event” by the system 100. An event can be defined as all the datacollected from the time a user enters into a pre-defined distance rangefrom the system 100 to the time the user exits this distance range. Thedistance can be calculated using ultrasonic sensors 102 (oralternatively LiDAR or other distance processes). The event includes allthe frames (or images) captured in this event bracket irrespective ofthe presence of a waste item in the user's hand (empty event).

The image data can be tagged with metadata, such as location identifier,system identifier, data/time, and so on. This metadata can be used bysystem 100 and/or a cloud server to generate analytic data and aggregateimage data using metadata. The image data can be accompanied by logs ormetadata. This can include the following example details: image name,location of image collected (system number and geographical location ofsystem 100), date/time when image frame was captured, any predictionsmade on the image, distance values for the accompanied ultrasonicsensors, and so on. In some embodiments, the image capture process canadd the above mentioned information in the image metadata. Additionally,any other image related information that can be useful in generatinginferences and trends of interest can be defined as configurations forcollection and can be added to the image metadata.

The sensors 102 can trigger the workflow for image processing using aproximity threshold, for example. The sensors 102 can distinguishbetween someone walking by, stopping to use a waste bin, and so on. Thesensors 102 trigger can be directed by a continuous increase inproximity. An example current proximity threshold is 5 metres but thisis just an example. There can be a larger range by stronger ultrasonicsensors 102, for example. There can be a smaller range in otherexamples.

The system 100 can be trained to detect a continuous decrease in theultrasonic (or sensor) range as a trigger for predicting the waste itemto be disposed. For instance, if after a few pulses by the ultrasonicsensors, it is determined that the range has continuously decreased byaggressive margins (e.g. greater than 50 cm/sec), then the predictiveengine starts to analyze the images captured by the camera 104. Outliersof this scenario include non-users who diagonally walk across the devicewithout disposing waste items, for example. If the ultrasonic range hasdecreased but stays the same then the system 100 detects that a user ispassing by parallel to the device.

The system 100 has the ability to recognize waste items from a distance,this makes anyone with a waste item within the systems 100 (e.g. sensors102, camera 104) field of view a user. In the specific case wherein auser is diagonally walking across the device without disposing the wasteitems, the system 100 might not want to filter out the frames and stillrecognize the waste items being carried across. This information can bevaluable for the purposes of training and understanding consumptiontrends for the particular location.

The continuous decrease metric (using the ultrasonic sensors 102) can becomputed by system 100 by aggregating the frame by frame delta in thedistance readings over ‘n’ frame pairs, where ‘n’ is an integer and aconfigured number of frames based on the facility wherein the system 100is installed. In other words, if there is a constant decrease in any oneof the ultrasonic readings for ‘n×2’ constant frames, then a user/objectis deemed to be approaching the system 100 in some examples.

The system 100 can be defined by a casing formed from a front panel,skeletal frame, and a rear panel to create an enclosure. In an exampleembodiment, the casing can appear to be a rectangular shaped object fromthe frontal view but is a complex mix of contours. In addition tocontaining the two ultrasonic sensors 102 and a camera 104, the casingis used to enclose the embedded hardware components (e.g. embeddedsupercomputer 108 with active heat sink, carrier 106) that operate toprovide a neural prediction engine for waste item detection andrecognition. The encasing can be equipped with waterproof andscratchproof features. This can allow it to be durable through rigorousjanitorial operations, increased physical interaction withobject-curious children, and/or vandalism, for example.

The system 100 can also have an HDMI port 110 (for connecting to anexternal display device or monitor). The system 100 can also have a USBport 112 for connecting to the camera 104. An example embodimentencloses the following components: Logitech C920 camera; UltrasonicMB1010 sensors; NVIDIA Jetson TX2 with an active heat sink; OrbittyCarrier; USB Connector Hubs. This is an example.

FIG. 4 is an exploded view of the system 100 of FIG. 1 . FIG. 5 is openstate view of the system of FIG. 1 .

The camera 104 can be placed equidistant from the two ultrasonic sensors102. In this example, the aggregate field of view of the two sensorscover 110-150° range and the camera's field of view is 78°. The system100 can have different positions or alternative configurations for thecamera 104 and ultrasonic sensors 102. Alternative exampleconfigurations that would capture sufficient data include placing thecamera 104 on top of the two sensors 102 or below them, forming eitheran upward pointing equilateral triangle, or downward pointingequilateral triangle.

The objective of the configuration is to obtain a sense of bodymovements of approaching users which can be accomplished via machinelearning processes (and neural networks). The system 100 uses a neuralprediction engine to analyse approaching body postures by scanningmultiple frames and determine if a user is approaching.

An alternative to gain a similar objective would be to implement anoptical flow that would be capable of determining the depth and distanceof every pixel within an image by feeding the system 100 a data set ofimages recorded by a depth sensor. Another alternative method would beto use LIDAR system. A LIDAR system is able to send out and receivemillions of pulses and compute hundreds of revolutions per second,building up a very accurate three dimensional map of its environment.This is a dynamic process and any moving object is quickly identifieddue to the constant change in the time for the pulse to bounce back fromthe object's surface. A LiDAR attachment to the system 100 can eitherreplace and/or complement the ultrasonic sensors 102 to computedistances of users moving towards/away from system 100 powered wastebins. This could increase the speed and accuracy with which adetermination of user presence is made. Furthermore, the system 100 maybe used to create and store three dimensional maps of the locationswhere systems 100 are installed to incorporate and provide indoorlocation intelligence as an additional service by studying location-wiseworkflows.

FIG. 2 is an overview of the placement of the system 100 of FIG. 1 . Thesystem 100 can connect to a display device 200 and can connect to awaste bin 202 (with a plurality of containers such as, compost, paper,recycle, landfill) by a connector 204. The external display device 200or screen can include an audio/visual display to dynamically provideaudio/visual feedback nudge users into dispose the waste items into acorrect container of the waste bin 202. Example feedback includes but isnot limited to: instructions for interacting with the system 100 and(container of) bin 202, reactive notifications of correct or incorrectdisposals of waste items into containers of bin 202, information on thelocal categorizations (that map to containers of bin 202), rewards foraccurate recycling, general advertising, and so on.

There can be a user profile and/or customer profile that maps to a setof systems 100 (e.g. a city user that manages 20 systems, each system100 identified by a system identifier and linked to the profile of thecity user). This can enable the cloud server to generate analyticsspecific to a user or benchmark data specific to a user, system 100, andso on. In some embodiments, there can be a mapping for the user profile.This mapping can be done on two levels: the user and the customer (e.g.airports).

On an individual user level, the system 100 can recognize keycharacteristics of people prior to sanitization and storing, forexample: age range, and modify the feedback (animations/messaging) tomake it more appropriate for the user. In this example, the nudgingprocess can have speed of (feedback) playback embedded within it as avariable, slowing down for an elderly user allowing for a longer andmore relaxed feedback nudging approach to ensure the user is comfortablewith following the sorting process, while making the feedback morecolourful and child friendly for a user who is a kid. These changes canbe embedded in the animation/nudging display algorithm and can adjustdynamically based on who the individual user is.

On a customer level, a cloud server can generate visual elements for adashboard interface to visualize all the systems 100 deployed at afacility (ex: an airport) or geographic area, for example, as nodes on amap in a cluster that share similar attributes. For this example, theairport system 100 can have features optimized for a “traveller” userprofile, accommodating the typical characteristics of users that mightbe found on an airport—for instance a language agnostic feedbacknudging/animation design to ensure that there is no language barrierimpeding accurate recycling as an airport can have users from all aroundthe world. Similar “customer level user profiles” can be created forother types of customers such as but not limited to office building,malls, universities, cities, etc. based on generalized demographic-basedcharacteristics found at these specific locations.

The feedback data can indicate a reward for a user based on a correctdisposal of the waste item, for example. For example, feedback data fora reward can include a QR code (or other machine readable indicia)display on device 200. A user can use their mobile device to scan the QRcode shown on the screen which can direct their device to a “Thank you”page, wherein the user inputs their name and email address, creating aprofile for the specific user allowing repeatability (user will onlyenter their name the following time). In some embodiments, system 100can be configured for associating faces of users with their rewardsprofile. The system 100 can recognize and store the faces of users (ifthey consent to participate in the rewards program) in a separate andsecure rewards database, and would query this database every time arewards-enabled user disposes an item making the process seamless. Onceregistered, the user can walk up to any of the system 100 placed at anylocation and dispose the waste items as shown on display 200. The system100 would automatically recognize the user as a registered rewardsmember and assign points accordingly. The user can see the status oftheir points at any time and can claim them with any one of oursponsoring partners as they please.

There can be a calibration process for the display 200 to align thevisual indicators with the containers of the bin 202 so that the system100 can adapt to different bin types, configurations of containers, andso on. The calibration process for the display 200 can be implementedwhen the waste bins are visual or not visible via the camera 104. Thiscan be done by analyzing the last predicted frame of a disposal eventand locating the centroid of the waste item. By calculating the shortestEuclidean distance, the system 100 can determine feedback data anddisplay (on device 200) the feedback data as visual indicators to theappropriate container of the bin 200.

The system 100 can be attached to the display device 200 which cangenerally be placed above receptacles or containers of a bin 202 forobject detection. The bin 202 shown is an example four waste streamprocess but the system 100 is independent of number of waste streamsthat can be placed below and arranged as the bin 202. The system 100 hasvision components that are triggered by the two ultrasonic sensors 102to commence the data flow and processing. The vision components captureimage data enabled by the camera 104 and processes the image data toidentify the object (or objects) approaching the system 100. The camera104 has a field of view that can capture image data depicting a wasteitem and a person carrying the waste item, for example.

The system 100 includes a supercomputer 108 (and carrier 106) thatprocesses the image data to detect a general shape of the person toestimate the location of the hands within the body. The supercomputer108 processes the image data to further distinguish the waste item inthe hands of the approaching user. The supercomputer 108 can configureconvolutional neural networks (stored in data store) and train theconvolutional neural networks (CNN) to identify the waste item andrecognize its destination among the different waste categories. In someembodiments, the supercomputer (embedded system) 108 runs inference forthe neural networks deployed on it. A cloud server can train CNNs forthe supercomputer 108, for example. The CN Ns are trained on the backendcloud server using deep learning process and the trained models can bedeployed onto the embedded systems on the bins with the supercomputer108. Hence, in some embodiments, the embedded systems only runinferences on networks that have been trained elsewhere (cloud server).Inference refers to confidence levels of predicting the items in framesbased on the training datasets. In some embodiments, training of theCNNs can be implemented on the embedded system (supercomputer 108).

The system 100 can predict or estimate that the item in the hands is awaste item. For example, when the face is detected, the area or regionbelow the face is where the system 100 assumes the waste item will beheld. In some embodiments, it is not expected that the waste item willbe held above the human torso. The system 100 can use a neuralpredictive engine trained for different types of objects (not limited towaste items) such cellphones and shopping bags. For such items, thesystem 100 might not run any predictions unless the user is extremelyclose to the device and is actively presenting the item to beidentified.

The CNN can be trained not only with processing several images that havebeen tagged with the correct category, but also via augmentations ofwaste item disposal event images. The system 100 (and/or cloud server160 of FIGS. 6 and 7 ) can augment images so that it can recreatedifferent settings, e.g. various light and contrast conditions, imageblurriness, etc. The system 100 can also use a generative adversarialnetwork that develops complex backgrounds. This can increase the pace inaccuracy and overall training of the neural prediction engine. Aftercapturing an image, the CNN can detect a face and immediately focus onthe area below it and around the torso. An identifiable object (e.g.similar looking waste items) can then be detected as the waste item tobe disposed. Other data points in the image can be filtered to focus onthe item in question.

Augmentations can refer to synthetic changes in original images withinthe dataset to diversify and strengthen the overall dataset. The morediverse the training data, the more ready the trained network will befor input situations (images) presented to it in various scenarios. FIG.8 is an example of a bottle image (first image is the original), thathas been augmented to rotate by 5 degrees, 10 degrees and through arange of gamma values to allow for brightness diversity.

The supercomputer 108 generates feedback data based on the detectedcategory (or categories). The display device 200 can then display thefeedback indicating predicted items and guide the user to throw orseparate items in the correct receptacles or containers of the bin 202.For items outside the standard waste stream categories, the system 100can provide feedback for sustainability and due process. For instance,if batteries were detected and then the user would be nudged to disposethem in the landfill, and a notification to the janitorial staff will besent to empty out the hazardous item.

An accurate disposal in the appropriate receptacle can be recognized bythe system 100 using object tracking. The user can be notified (atdisplay device 200 with additional feedback and provided with a reward(such as a coupon) that could be attained via different ways such asscanning a QR code for redemption at a store, for example.

For example, based on the movement of the user, the ultrasonic sensors102 can detect if the item is placed in the correct receptacle of thebin 202. A detected movement towards a receptacle that is notrecommended by the system 100 can trigger the display device 200 tonotify the user of the mistake and try to guide the user the correctrecycling procedure using additional feedback. The intensity of there-education can be customized by, for example, the property managementwho can have access to a dashboard 306. This notification may give theuser a chance to rectify the inaccurate disposal of the waste item.

The movement of the user can be determined once the visual indicator isprovided at display device 200 as the guide to ensure the user correctlydisposes of the item. The camera 104 can capture the waste item in animage and the ultrasonic sensors 102 can measure the closeness of theuser's body as the user is getting closer to the receptacles to disposethe waste item. In each frame the object's location relative to thereceptacles can be calculated. The neural prediction engine cancalculate the waste item's centroid and its distance from therecommended receptacle. As the waste item's centroid is closing itsdistance towards the any other receptacle other than the recommendedreceptacle, the system 100 can inform the user that the disposal isincorrect.

FIG. 3 is an information flow diagram of a process 300 for a wastediversion system process according to an embodiment. The system 100 hasvision components that are triggered by the two ultrasonic sensors 102to commence the data flow of the process 300.

The process 300 indicates activation of a neural predictive engine foroperation of the system 100 and continual learning/updating of thesystem 100. Aspects of the process 300 and engine can be implemented ata cloud server 160 (FIGS. 6 and 7 ). In some embodiments, the process300 can be implemented (or portions thereof) on a backend serverconnected to system 100. In some embodiments, the process 300 can beimplemented (or portions thereof) on the system 100. The backend servercan be connected to a network of systems 100 to collect and processimage data from multiple systems 100.

At 302, the process of sanitization begins when the engine receives theframes (image data) captured by the camera 104 to be processed forfurther image analysis. These images contain faces and sanitization caninvolve blurring the faces (instantaneously) as part of the waste itemidentification process in the neural prediction engine. In someembodiments, the system 100 can implement sanitization of the images toblur detected heads. The processed images of waste items can betransmitted to a cloud database and server for further sanitization,such as the blurring of missed faces in the previous stage. As noted,the process can be implemented at a backend server 160 or clouddatabase.

The blurring process is not only directed to faces but also any shapethat is indicative of a person's head, such as the side profile of aface or the back of users' heads. This is to blur and block any personalidentifiable information, for example. The system 100 (and/or cloudserver 160) can make sure the detected heads in the background aresanitized from the image data as well while processing the focus on theuser who has walked up to the bin to dispose of waste items.

The blurring process can detect data indicative of a person's head.There can be a training phase with person features to define headcharacteristics, for example. For example, the system 100 and/or cloudserver 160 can develop a face network using face datasets, to get abaseline face network started. This model might be good enough to blurany frontal or profile face angles, allowing for an effective firstsanitization run. Following this, all frames with heads (facing thecamera at any angle) can be annotated and added to the existing facedataset making it more robust and inclusive of a head at all angles.

At 304, the waste prediction data (image data) can be processed togenerate waste insight metrics. The waste insight metrics can be outputfor display as part of a dashboard interface 306. In some embodiments,there can be an added step of human validation that verifies thesanitized data which will then be inputted into the retraining of theengine. At 308, false positives and items with low accuracy can beverified and the data used for retraining of the system 100 (andengine). At 310, validation of the engine can result in the firmwareupdates to the system 100 which allows for a continually improved system100. The system 100 includes an engine for detection of waste itemswhich can be updated at 310 for continuous learning.

The system 100 can implement automated waste audits. As each system 100detects waste items, the system 100 stores the type of items disposedinto each waste stream. The system 100 can generate metadata or wasteinsight metrics to predict volume of each waste receptacle of the bin202 based on the item disposed and to calculate diversion rates. Theimage data can be tagged with the computed metadata. In someembodiments, the metadata can be linked to an identifier linked to theimage data. The system 100 transmits all collected metadata (along withthe associated image data, or an identifier linked to the image data) tothe cloud server to be accessed (near) instantaneously. The dashboard306 can display computed data (e.g. metadata, insights) that tracksassigned systems 100 within any given premise. That is the dashboard 306can display data for a set of systems 100. The dashboard enablesproperty managers to view real time waste analytics for a system 100 ora set of systems 100. Furthermore, a waste audit report can be tabulatedinstantaneously due to the capabilities of the neural prediction engineand the computed waste insights.

In some embodiments, the system 100 can implement brand recognition andtrigger the display of corresponding advertisements or reward on displaydevice 200, for example. A sanitized waste item frame taken by thevision system 100 and stored in the cloud database at the cloud servermay be further processed to extract useful information, such as brandsindicated in the image data. For example, the cloud server can extractbrand information from the image data and provide this information tothird parties. The brand information can include data relating to thebrands of the items being thrown away. This consumption dataset storedat could be passed on to other systems 100 as a way to retrain theartificial intelligence engine. The system 100 can provide feedback datato reward the user from the brand if a partnership is established, forexample. An alternative is also the collection of brand data that couldbe used for advertising at display device 200.

FIG. 6 is a diagram of a system 100 for waste item detection andrecognition depicting an example physical environment.

The system 100 can include an I/O Unit 602 (with sensors 102, camera(s)104, display 200), a processor (supercomputer) 108, communicationinterface 604, memory 608, and data storage 608. The processor 108 canexecute instructions in memory 606 to implement aspects of processesdescribed herein. The processor 108 can execute instructions in memory606 to configure detection unit 120, analytics unit 122, feedback unit124, sanitization unit 126, brand unit 128, neural networks 130, andother functions described herein. The system 100 may be software (e.g.,code segments compiled into machine code), hardware, embedded firmware,or a combination of software and hardware, according to variousembodiments.

The system 100 is configured for artificial intelligence driven wasteitem recognition and recycling education. The system 100 has a modulewith two ultrasonic sensors 102 to trigger the vision components and acamera 104 (e.g. as part of an I/O Unit 602) to capture image data forprovision to detection unit 120 to detect waste items. The module canconnect to a connector for attachment to a bin 202 and/or display device200. The feedback unit 124 can generate feedback based on the processedimage data. The display device 200 (e.g. as part of an I/O Unit 602) hasa screen to display identified waste items and feedback to nudge usersto dispose them in the appropriate waste receptacle. For example, thefeedback can indicate the correct receptacle of the bin 202.

The detection unit 120 includes a neural prediction engine whichutilizes machine learning (and neural networks 130) to recognise wasteitems and map the waste items to a category based on a list of classesfor different waste items. Through each image that is being processed bythe detection unit 120, the following example data points can picked up:person features, objects, environment. Example person features includethe method of holding a waste item, the hands of the users, and so on.Example object data includes pairing of different waste items. Exampleenvironment data includes background elements, lighting of objects andusers. The training data points for the person features, objects,environment can then transformed into classifiers or class definitionsfor real-time detection. For example, this can involve adding a “person”class to the training dataset, which can be annotated with frameswherein persons are present. The persons, objects and environments canbe part of the dataset used for training the neural network. Duringtraining, the network computes features from data points to learn uniqueattributes about each of these classes. Once the training is completethe network has learnt to classify different objects, environments andthe features of persons or any combinations of these uses the trainedclasses.

FIG. 9 illustrates sample training images for the class “coffee cup”.The dataset (for class coffee cup) can have thousands of images likethese examples (and augmentations of these images). The dataset includesa wide variety of coffee cups displayed with different backgrounds andangles.

Each waste item has a uniquely assigned class or definition so that thedetection unit 120 can classify the detected item in the image data.Each waste item has its own training class. Fundamentally, each classhas a certain number of training images and the neural network 130 goesthrough a thousands of steps (iterations) traversing the dataset inbatches to learn features about each image and associating them with thelabelled classes (per image). Over thousands of steps, the network's 130error function stabilizes meaning that it learns to generalize imageinputs and make predictions about the classes they are most likely to bein. This means that for each image passed through the neural network 130(e.g. shown to the network), there is an output confidence levelreferring to how confident the neural network is about its predictionfor an image belonging to a certain class. The system 100 can set theconfidence level to a confidence threshold and if the predictionconfidence drops below the threshold, the image is flagged as unsure forvalidation. All flagged images are then re-annotated and retrainedallowing for the network 130 to learn from its low confidencepredictions and improve its overall accuracy. If the system 100 isunable to classify an image, it shows the user the most commonlydisposed waste item in an area, allowing for probabilities in favour ofthe highest likelihoods based on past patterns.

The detected (and classified) waste items can be used by feedback unit124 to provide feedback at display 200 to nudge users to dispose themaccurately while simultaneously educating them. The detection unit 120uses object detection that utilizes computer vision to identify a wasteitem and apply unique predictive processes. The categorized waste itemsis used by feedback unit 124 to generate feedback to nudge the users indisposing the waste accurately in the appropriate waste receptacle.

The detection unit 120 uses trained neural networks 130 to process thedata pipeline. The analytics unit 122 can generate insights on validatedwaste predictive data or insights. The data can include but is notlimited to: type of waste item; item disposed in waste stream; productbrand of waste item; dimensions of waste item; volume of waste item;public engagement metrics; updated diversion rates of waste items;individual receptacle fill level.

The brand unit 128 can generate brand or advertisement data for displayat device 200. This can include rewards, for example. For example in anapplication, Face detection triggers the immediate blurring and removesany personal identifiable information. After scanning a captured frame,detection unit 120 locates the waste item within the image. The brandunit 128 can apply a brand image classifier in real time to recogniseany of the brand logos that it is previously trained to identify. Brandlogos and their variations can be continuously updated on system 100 andlinked to corresponding waste items.

As shown, the system 100 has sensors 102 for detecting an approachingobject to trigger a camera 104 to capture image data and the processor108 to process the image data. The sensors 102 detect the approachingobject by computing a continuous decrease in signal range for theapproaching object, for example. The system 100 uses the camera 104 forcapturing image data of the approaching object. The system 100 hasnon-transitory computer readable storage medium with executableinstructions for causing the processors 108 to use the detection unit120 to process the image data using a neural network 130 to detect awaste item within the image data and determine a category for the wasteitem. The neural network 130 defines classes for different waste itemsand maps the detected waste item to the classes for the different wasteitems to compute a pairing of the detected waste item and a class. Theclass is associated with the category for the waste item. The categorycan map to a receptacle of the bin 202, for example. The feedback unit124 generates feedback data indicating the category for the waste itemand an indication of an appropriate receptacle (of the bin 202) todispose of the detected waste item. The system 100 has a display device200 for displaying the feedback data. In some embodiments, the feedbackdata indicates the location of the appropriate receptacle to dispose ofthe detected waste item. In some embodiments, the neural network istrained through consecutive object detection to determine a correctreceptacle that the waste item should be disposed and this can map tothe appropriate receptacle to dispose of the detected waste item.

In some embodiments, the detection unit 120 is configured to use theneural network 130 estimate a location of a head of the object withinthe image data. The detection unit 120 can detect the waste item in theimage data using the estimated location of the head within the imagedata. For example, the head location can be used to determine orestimate hand location and the waste item is proximate to the handlocation, for example. The waste item might be below the location of thehead, for example.

In some embodiments, the neural network 130 is trained using image datato define data points for person features, waste items, background andenvironment. These features can be used to process real-time image datato determine regions of the image data the relate to person featuressuch as hands, head, and so on. In some embodiments, detection unit 120is configured to use the neural network 130 to detect data points withinthe image data. The data points can correspond to a set of the personfeatures, the set of the person features defining head features forestimating the location of the head of the object. In some embodiments,a portion of the data points corresponding to the background and theenvironment which can be used filtering the image data to focusdetection unit 120 on the detected waste item within the image data. Insome embodiments, the detection unit 120 is configured to use the neuralnetwork 130 estimate a location of hands within the image data based onthe location of the head and detect the waste item in the image datausing the estimated location of the hands within the image data. In someembodiments, the processor 108 is configured to use the neural network130 to detect person features within the image data, the person featuresdefining hand features for estimating the location of the hands.

In some embodiments, the processor 108 is configured to use the neuralnetwork 130 to detect person features within the image data, the personfeatures defining head features for estimating the location of thehands.

In some embodiments, the camera 104 and the sensors 102 captureadditional data indicating disposal of the waste item. The processor 108determines an appropriate receptacle to dispose of the detected wasteitem, uses the additional data to determine whether the waste item wasdisposed in the appropriate receptacle. The feedback unit 124 cangenerate additional feedback data based on the determination. Theprocessor 108 is configured to measure closeness of a user to theappropriate receptacle using data from sensor 102 and/or camera 104, forexample. In some embodiments, the camera 104 captures additional imagedata indicating disposal of the waste item. The processor 108 determinesan appropriate receptacle to dispose of the detected waste item and usesthe additional image data to determine whether the waste item wasdisposed in the appropriate receptacle. The processor 108 generatesadditional feedback data based on the determination. In someembodiments, the display device 200 displays the additional feedbackdata. In some embodiments, upon determining that the waste item wasdisposed in the appropriate receptacle, the processor 108 generates areward for redemption as the additional feedback.

In some embodiments, the sanitization unit 126 is configured to detecthead data within the image data and blur the head data to generatesanitized image data. The sanitization unit 126 can use the personfeatures, for example. In some embodiments, the neural network 130 istrained using image data to define data points for person features. Theperson features define head features for detecting the head data withinthe image data. The sanitization unit 126 can use the neural network 130for the blurring process.

In some embodiments, the processor 104 is configured to tag the imagedata with metadata indicating a system identifier, category for thewaste item, location data, and time data. In some embodiments, a cloudserver 160 is configured to receive the image data tagged with themetadata. The cloud server 160 can use the metadata to generate visualelements for dashboard 306. For example, cloud server 160 can use thelocation data to generate visual elements for dashboard 306 that relateto systems 100 at a specific location region. As another example, cloudserver 160 can use the category data to generate visual elements fordashboard 306 that relate to disposal of waste items of a particularcategory across a network of systems 100. Other dynamic elements fordashboard 306 can be generated by cloud server 160.

In some embodiments, the cloud server 160 is configured to process theimage data to detect head data within the image data and blur the headdata to generate sanitized image data. That is, cloud server 160 canimplement an additional sanitization process to ensure all head data isblurred. In some embodiments, the cloud server 160 is configured toreceive the image data tagged with metadata indicating the category forthe waste item, validate the image data, generate a firmware upgrade forthe neural network 130, and transmit the firmware upgrade to the system100 to update the neural network 130.

In some embodiments, the analytics unit 122 is configured to computeimage analytic data including types of waste items, volume of individualwaste item, monitored volume of each receptacle based on the waste itemsdisposed, and calculated diversion rate. The following provides anexample of how some metrics can be computed by analytics unit 122.

The analytics unit 122 can compute dimensions of waste items using afoundational dataset for dimensions of popular waste items (e.g. coffeecups, bottles, cans, plastic wrappings/bags). Combining this with thedistance calculated of the waste item by ultrasonic sensors 102, thedetection unit 120 (neural prediction engine) maps it across apredefined list to determine what dimensions corresponds to the givendistance datapoints. Each captured frame is pixelated and each pixel isscaled for the given distance in that frame. The number of pixels ittakes to form the waste item in the image is calculated and crosschecked with the rest of the frames. For instance, at a distance of 50cm from the camera, a medium sized coffee cup should occupy 40 pixels onthe captured frame.

The analytics unit 122 can compute the volume of waste item using afoundational dataset for volumes of popular waste items (e.g. coffeecups, bottles, cans, plastic wrappings/bags). Combining this with thedistance calculated of the waste item by ultrasonic sensors, thedetection unit 120 (neural prediction engine) maps it across apredefined list to determine what volume corresponds to the given objectat a specific distance.

The analytics unit 122 can compute public engagement metrics ondifferent levels. An example level is for once the system is active(displaying visualizations on the screen of device 200), passerbyimpressions can be counted as gaze estimation (deep learning process)towards the receptacles. The analytics unit 122 can calculate the numberof seconds the passerby is viewing the display screen by counting thenumber of consecutive frames gaze estimation algorithms. This might notinclude the disposal of waste items.

An example level is for once the event disposal takes place and the useris leaves without staying for the additional information displayed onthe screen of device 200, including the QR codes for a myriad of rewardsfrom brand partners. An example level is for once the user has stayedafter disposing the waste item, scanned the displayed QR, and has beendirected to the partner brand's website.

The analytics unit 122 can compute updated diversion rates of wasteitems once diversion rates will be updated at the end of each day, andcan be calculated as follows:

$\frac{\sum\left( {{{Volume}{of}{Organics}{Receptacle}} + {{Volume}{of}{{Recyc}{lable}}{{Recept}{acle}}} + {{Volume}{of}X{Waste}{Stream}}} \right)}{{Volume}{of}{All}{Receptacles}\left( {{includes}{landfill}} \right)}$

The analytics unit 122 can compute individual receptacle fill levelmetrics. As each item is identified, a record of the item's dimensionsare stored and added onto the volume of the receptacle it wasdesignated. The analytics unit 122 can build a set the volume of eachreceptacle. A confirmation of the item disposed in the correctreceptacle acts as a crosscheck of the volume of each receptacle.

The system 100 implements a process for providing feedback to nudge theuser to dispose the waste item into a receptacle. The process caninvolve the detection unit 120 for detecting the waste item to generatean predictive model based on previous training. The detection unit canbe configured to analyze the image using a neural network 130 todetermine a category for the waste item. The feedback unit 124 candetermine an appropriate receptacle for the waste item based on thecategory and generate feedback data that can trigger the display of adynamic reaction (motion graphic) on the screen indicating to the userthe appropriate receptacle for the waste item.

The feedback unit 124 can implement a process for rewarding a user basedon accurate recycling by providing myriad ways such as scanning a QRcode for redemption at a partner's location. This interaction with thesystem 100 provides an engagingly intuitive and positive recyclingexperience.

The system 100 uses sensors 102 to trigger a process for activating thevision system enabled by the camera 104 to identify the objectapproaching the bin 202 and module of the system 100. The system canestimate if the shape (e.g. human) approaching is predicted to dispose awaste item to trigger camera(s) 104 to capture video data. The detectionunit 120 can process the image data to estimate the location of thehands within the shape and further distinguish the waste item in thehands of the approaching shape.

The system 100 can implement a training process (of neural networks 130)to enable detection unit 120 to focus on items in the hands of the videoor image data. The training can be implemented at the cloud server 160,the system 100, or a combination thereof.

Through each image that is being processed by the system 100 (anddetection unit 120), the following example data points can picked up:person features, objects, environment. Example person features includethe method of holding a waste item, the hands of the users, and so on.Example object data includes pairing of different waste items. Exampleenvironment data includes background elements, lighting of objects andusers.

Training images can be obtained by attaching recording sensors next tobins. After the images (data) are recorded, sanitizing can involveblurring of faces so that waste items become the focus of analysis.Annotating begins after bounding boxes are defined to indicate where thewaste item would be located by running the sanitized images through amachine learning system that does the preliminary round of waste itemrecognition. Next, there is a validation process that is conducted byannotation to catch the items that were not annotated or missed by themachine learning system.

The annotation of image data can be a step by step process (that can belocation specific) for a number of classes. Each system 100 can bedeployed with a baseline model (embedded model) with an architecturethat is optimized for running it on the edge, causing some accuracytrade-offs. The baseline model can be trained at cloud server 160 toinitiate the automated annotations pipeline and trained with the samedataset as the systems 100 but with a much deeper architecture (theannotations model), as this can be run on GPUs without the constraint ofhardware resources as is on the edge of the system 100, for example.

The automation process can implement the following operations for agiven location. All data coming in from a given location can passthrough the annotations model, allowing for it to pick up on frames thatmight have been missed (predictions) by the embedded model and predictitems that it has been trained on. This first sweep through theannotations model can allow for a large fraction of unpredicted frames(on the embedded model) to generate predictions, still leaving a certainfraction of frames left unpredicted. This phase can involve validationto check the predicted frame with a certain accuracy threshold to ensurethat the less confident predictions are indeed true positives andmanually annotate the frames that were missed by both models.

The next phase involves retraining both models (embedded and annotationsmodel) with the newly labelled data points (first by the annotationsmodel and then by the validation stage). Both these models can nowreplace their previous versions of the models in the workflow. This isone cycle and is restricted only by the resources in terms of ourworkforce and training hardware. As this cycle is repeated a number oftimes, the annotations model can become accurate and flexible enough torecognize any item from the trained class list for a given location.Once this is achieved, all new data can simple be piped through themodel for annotations.

The same process can be replicated at multiple locations with multiplesystems 100 and their datasets, creating multiple annotations model. Thecollective dataset can then be merged to create more powerfulannotations model capable of annotating waste data for any given area.

The sanitization unit 126 implements a process for sanitization of imagedata where captured frames of waste items are processed using faceblurring in the neural prediction engine. The system 100 can transmitthe processed images to the cloud server 160 and cloud database 170where further collective and more robust neural sanitization takes placesuch as blurring of remaining faces that may have been missed in theprevious stage.

In some embodiments, the system 100 implements a process for providingsmart waste analytics via the dashboard 306 by aggregating the collecteddata such as the type of items disposed into each waste stream, volumeof individual waste item, monitored volume of each waste receptaclebased on the item disposed and calculated diversion rates. The system100 transmits all collected metadata to the cloud server 160 to beaccessed (near) instantaneously.

The I/O unit 602 can enable the system 100 to interconnect with one ormore input devices, such as sensor(s) 102, camera(s) 104, a keyboard,mouse, touch screen and a microphone, and/or with one or more outputdevices such as a display screen 200 and a speaker.

The processor 108 can be, for example, a supercomputer (with an activeheat sink) or any type of general-purpose microprocessor ormicrocontroller, a digital signal processing (DSP) processor, anintegrated circuit, a field programmable gate array (FPGA), areconfigurable processor, or any combination thereof.

Memory 606 may include a suitable combination of any type of computermemory that is located either internally or externally such as, forexample, random-access memory (RAM), read-only memory (ROM), compactdisc read-only memory (CDROM), electro-optical memory, magneto-opticalmemory, erasable programmable read-only memory (EPROM), andelectrically-erasable programmable read-only memory (EEPROM),Ferroelectric RAM (FRAM) or the like. Data storage devices 608 caninclude memory 606, databases 610 (e.g. graph database), and persistentstorage 612.

The communication interface 604 can enable the system 100 to communicatewith other components, to exchange data with other components, to accessand connect to network resources, to serve applications, and performother computing applications. The communication interface 604 caninclude ports, for example. The communication interface 604 can connectto a network 140 (or multiple networks) capable of carrying dataincluding the Internet, Ethernet, plain old telephone service (POTS)line, public switch telephone network (PSTN), integrated servicesdigital network (ISDN), digital subscriber line (DSL), coaxial cable,fiber optics, satellite, mobile, wireless (e.g. Wi-Fi, WiMAX), SS7signaling network, fixed line, local area network, wide area network,and others, including any combination of these.

The system 100 can be operable to register and authenticate users (usinga login, unique identifier, and password for example) at dashboard 306prior to providing access to applications, a local network, networkresources, other networks and network security devices. The system 100can connect to different machines, data sources 150 (linked to databases160), other systems 100, cloud server 160, and so on.

The data storage 608 may be configured to store information associatedwith or created by the system 100, such as for example image data, wasteitem categories, configuration data for the location of the receptaclesof the bin 202, advertisement data, reward data, and so on. The datastorage 608 may be a distributed storage system, for example. The datastorage 608 can implement databases, for example. Storage 608 and/orpersistent storage 612 may be provided using various types of storagetechnologies, such as solid state drives, hard disk drives, flashmemory, and may be stored in various formats, such as relationaldatabases, non-relational databases, flat files, spreadsheets, extendedmarkup files, and so on.

FIG. 7 is a diagram of a cloud server 160 depicting an example physicalenvironment. The cloud server 160 can connect to multiple systems 100 tocollect data for waste items and generate waste item insights fordisplay at dashboard 306. The cloud server 160 can process waste itemdata using detection unit 722, use the results to train neural networks730 (via training unit 726) and generate firmware updates 728 forsystems 100 for continuous learning.

The cloud server 160 can include an I/O Unit 702, a processor 704,communication interface 706, memory 708, and data storage 710. Theprocessor 704 can execute instructions in memory 708 to implementaspects of processes described herein. The processor 704 can executeinstructions in memory 606 to configure sanitization unit 720, detectionunit 722, analytics unit 724, training unit 728, firmware update 728,neural networks 730, and other functions described herein. The system100 may be software (e.g., code segments compiled into machine code),hardware, embedded firmware, or a combination of software and hardware,according to various embodiments.

In some embodiments, the cloud server 160 implements a process forproviding smart waste insights via the dashboard 306 using analyticsunit 724 to aggregate the collected data such as the type of itemsdisposed into each waste stream, volume of individual waste item,monitored volume of each waste receptacle based on the item disposed andcalculated diversion rates.

The cloud server 160 can implement further sanitization of images toblur head data using sanitization unit 720. The detection unit 722,analytics unit 724, and training unit 728 can implement (at leastaspects of) process 300 (FIG. 3 ) for validating data, retrainingsystem(s) 100 and generating firmware updates 728.

The training unit 728 can train neural networks 730 (and engine) forprovision to system 100 (e.g. as firmware updates 728 or initialconfigurations). Data can be collected for training from sensors andcameras that are placed out into the open, for example. This can be afoundational dataset that can be installed on system 100 when placingthe system 100 in new premises. After a designated time interval, thetraining of identifying waste items particular to the new premises canbe implemented and that incremental dataset can be used to improve thefoundational dataset. The data can be tagged with an identifier forsystem 100 to identify a set of data that is particular to a system 100(or location, for example). Waste items in a new environment can beunderstood by placing the foundational dataset, that is beingincrementally improved, and through training, learns to grow its neuralprediction engine for the specific premises.

Frames fed to the training unit 728 for retraining can occur at regularintervals after sanitization has been thoroughly executed. A batch offrames that have been sanitized and annotated by a process of validationcan be fed into the training unit 728 for retraining and the latestupdated model will be sent for firmware updates 728.

Further example details relating to I/O Unit 702, a processor 704,communication interface 706, memory 708, and data storage 710 aredescribed in relation to similarly named components of FIG. 6 , forexample.

The analytics unit 724 can compute different waste item metrics such asis described in relation to analytics unit 122 (FIG. 6 ), for example.The analytics unit 724 can compute different waste item metrics fordisplay at dashboard 306.

For example, analytics unit 724 can compute metrics for the monitoredvolume of each receptacle based on the item disposed. As mentionedabove, when each item is identified, a record of the item's dimensionsare stored and added onto the volume of the receptacle it wasdesignated. A confirmation of the item disposed in the correctreceptacle acts as a crosscheck of the volume of each receptacle. Forexample, analytics unit 724 can compute metrics for calculated diversionrates. As mentioned above, the daily diversion rates can be calculatedas follows:

$\frac{\sum\left( {{{Volume}{of}{Organics}{Receptacle}} + {{Volume}{of}{Recyclable}{{Receptacl}e}} + {{Volume}{of}X{Waste}{Stream}}} \right)}{{Volume}{of}{All}{Receptacles}\left( {{includes}{landfill}} \right)}$

The discussion provides many example embodiments of the inventivesubject matter. Although each embodiment represents a single combinationof inventive elements, the inventive subject matter is considered toinclude all possible combinations of the disclosed elements. Thus if oneembodiment comprises elements A, B, and C, and a second embodimentcomprises elements B and D, then the inventive subject matter is alsoconsidered to include other remaining combinations of A, B, C, or D,even if not explicitly disclosed.

In another aspect, embodiments can relate to a waste bin, and moreparticularly to a waste bin with automated detection and routing ofwaste items into appropriate receptacles.

According to an embodiment as shown in FIG. 10 a waste bin 1000 may havea single opening 1100 for receiving waste (or the system 100, forexample). As shown in FIG. 11 , within waste bin 1000 may be separatereceptacles 2100, 2200, 2300 for receiving different categories ofwaste. For example, receptacle 2100 may be for recyclable waste,receptacle 2200 may be for compostable waste, and receptacle 230 for allother waste. The number of receptacles and categories of waste receivedtherein may vary according to local (municipal) standards for wastecollection and recycling.

Waste bin 1000 may be defined by a casing formed from front panels 1200,1250A, and 1250B, frame 1300, and rear panel 1400 to create anenclosure. As shown, waste bin 1000 is a rectangular prism, however,other shapes, such as oval prisms, cylinders and cubes may be used aswell. Receptacles 2100, 2200 and 2300 are disposed in the lower portionof waste bin 100, with a diverter 2400 in the upper portion which actsto direct waste items inserted into opening 110 into the appropriate oneof the receptacles 2100, 2200, 2300 according to the type of waste. FIG.10 shows the waste bin 1000 assembled with the front panels 1200, 1250A,and 1250B open. In FIG. 10 , front panels 1250A and 1250B form twohalves to provide access. However, front panels 1250A and 1250B couldalternatively form a single piece, as this may enable bin 1000 to bemore easily manufactured or accessed.

Diverter 2400 may include an imaging system (or integrate with system100) to take an image of the waste item inserted into the waste bin1000. The imaging system may include one camera or multiple cameras ormay be a sensor suite which may contain one camera or multiple camerasor other devices. The imaging system may include ultrasonic sensors andspectrometers, to further assist in the identification of the waste itemor to provide other desired information. The sensor data may be includedin metadata and associated with the image. The imaging system may bemounted on a holder suspended from the underside of the top of frame1300. The holder of the imaging system may alternatively be secured toany surface of bin 1000 so that the sensors or cameras of the imagingsystem are able to image or sample items which have come or are comingthrough opening 1100. The imaging system output is then analyzed todetermine the waste type of the waste item and this information may beused to control the diverter 2400 such that the waste item is directedinto the appropriate receptacle without any user intervention.

Bin 1000 may include a supplemental sensor suite of cameras or othersensors within the interior of bin 1000 for waste level detection andmaterial composition detection.

As shown in FIG. 14 , an external surface such as rear panel 1400 mayfurther include an audio/visual display 1500 to present information tousers. Such information may include instructions for using the waste bin1000, information on the local categorizations, or general advertising.

As shown in FIG. 13 , an item routing process may begin by capturing animage of the waste item at step 4100. The image is captured when theuser disposes of a waste item through the opening 1100 in the waste bin1000. The imaging system may first create the appropriate lightingconditions to capture an image of the item using the camera and/or othersensors or lighting devices. The imaging system may then capture animage (or other sample) of the item using the camera and/or othersensors. The image is then stored (step 4200) in a database, which maybe local to bin 1000 or remote to bin 1000. Prior to the image beingstored in step 4200, it may be labeled with a time stamp or locationstamp or other metadata such as data obtained by ultrasonic sensors orspectrometers.

The image may then be passed through an image classification neuralnetwork (step 4300) to identify the waste item captured therein. Theimage classification neural network identifies the item (e.g. a plasticbottle) and determines the appropriate category and receptacle for theitem (e.g. recycling). The neural network may vary in layer depth.

As indicated by the arrow in FIG. 13 from the step of passing an imagethrough an item identification network (step 4300) to the step ofcapturing a waste item image (step 4100), the system may be incontinuous detection mode to capture images and identify items. Thesystem may only trigger the step of routing the item (step 4400) when anitem is placed in bin 1000 and identified as an item. In someembodiments, the system remains in detection mode regardless of whetheran item is being routed or other steps, such as the steps indicated inFIG. 13 , are being taken.

Training data for the neural network may be provided initially fromexisting image databases, which may be subsequently augmented usingimages, such as curated images, of real waste items. Collection oftraining data may also be a continuous process, and may be facilitatedby gathering images from every location where a waste bin 1000 isplaced. Each bin location may be monitored closely for the waste data itcollects in a pre-determined period of time, such as over the course ofa day. This data may be used to train the networks and may also begathered one or more database for other purposes, such as monitoringwaste disposal volume or redirection efficiency. The system may beretrained periodically as new data is introduced, further strengtheningthe network capabilities.

In an event where a complex item (ex: an item, or collection of itemsdisposed of together, which includes organics, recyclables, and landfillitems) is disposed into the single opening 1100, the algorithm may lookto the category with the highest surface area and make a routingdecision in favour of the category that would benefit most fromreceiving this complex item.

In an event where it is determined that a waste item has beenmiscategorised, the system may be retrained by adding the corrected datainto the network to ensure the same error is not repeated twice. Thefrequency of the training may determine the overall efficiency of thesystem, with more frequent training leading to greater efficiency.

Once identified, the item may be routed (step 4400) to the appropriatereceptacle (2100, 2200, 2300) within the waste bin 1000. According to arouting algorithm adjusted for the local waste disposal regulations,such as local municipal waste management regulations, a signal is sentto diverter 2400 to position or adjust or operate it such that the wasteitem is routed to the appropriate receptacle. As shown in FIG. 11 , thediverter 2400 may include X-shaped fins (‘X-fins’) which are rotated tocreate a route for the waste item to the appropriate receptacle and, ifnecessary, provide a motive force to move it along the route. Forexample, once the disposed item has been identified, the routingalgorithm may direct bin 1000 to power up the right motors to turn theappropriate X-fin buffer or buffers. The motor or motors may then turnthe appropriate buffer or buffers to the right or left based on thecategory identified, to direct the item into the correct receptacle. Thefinal categorization of the waste item may then also be associated withthe captured image, which may be stored in one or more databases.

The X-fin motors and other powered components of bin 1000 may beelectrically powered. Power may be provided by sources such as batteriesor solar panels, or may be provided by standard 120/240 V wall power.

An image or other sensor data taken by the imaging system and stored inthe image database in step 4200 may be further processed to extractuseful information. For example, there may be value in extractingbranding information and providing this information to third parties;information concerning the brands of the items being thrown away. Insome embodiments, the provision of branding information could beprovided to third parties for a fee or could be included in an audit. Atstep 4500 the image stored in the image database may be passed through aselective search algorithm to isolate brand information. This mayinclude isolating different areas of the image for processing, and mayalso create new image objects for analysis. This process may take placein parallel with the item identification and routing process or may takeplace subsequent to the identification and routing process.

For example, the item recognition neural network may recognize brandinginformation in addition to category determination, and may applybranding information as metadata to the image.

However, branding information can be extracted from an imagesubsequently to the routing process. For example, once an image of awaste item has been categorized for appropriate routing, the image maybe sent at step 4200 to an image database, which may be an externalcentral database serving a plurality of bins. From the image databasethe image may be drawn to be processed by a neural network for thedetection and classification of branding or logos at step 4600. Thebrand recognition neural network drawing the images from the imagedatabase for branding detection may be the neural network used for itemrouting or may be an additional neural network, such as a central neuralnetwork dedicated to brand detection and identification. This brandrecognition neural network may vary in layer depth.

The use of the image categorization neural network for brand recognitionmay reduce the cost and complexity of the process. However, the use of asecond neural network, which may be a central neural network common to aplurality of bins, may be desirable as the categorizing and routing ofan object is time sensitive. As brand recognition may not be as timesensitive as item identification and may require a higher accuracy,having a second network on the back end dedicated to this operation mayallow the system to take additional time to ensure a high level of brandrecognition accuracy and to thoroughly analyze the entire image toextract as much branding information as is desired.

A full image may be relatively large, and step 4600 may require that animage be thoroughly searched to locate a brand logo in order to run arecognition algorithm on it. Accordingly, step 460 may involve breakingthe image down into multiple area-wise chunks. The use of multiplearea-wise chunks may assist in examining all parts of the image inanalyzing for brand details. The images of disposed items are brokendown into smaller areas or area wise chunks using a selective searchalgorithm which may break an image down into smaller components based onsimilarities in parts of the image. For example, a green logo on a whitecup may be a distinctive feature; the selective search algorithm catchesthis delta and separates the image into two chunks: a white area and agreen area. The selective search algorithm may be used to separateimages into multiple sub-images based on color differences or deltas.The brand recognition neural network analyzes each chunk to recognizebrands that may be present on the surface of the chunk.

In some embodiments, the imaging system may include multiple cameras orother sensors positioned to take images or sensor data from multipleangles around an item at step 4100. In addition to helping the itemidentification neural network identify the item, this may ensure thatbranding data found anywhere on an item is located to be added to theitem.

In some embodiments, the brand recognition and item identificationprocesses may include an ensemble approach. The use of an ensembleapproach (the use of multiple learning algorithms to obtain betterpredictive performance) with the neural network or neural networks mayensure a higher accuracy of item identification or brand recognition.

Brand recognition data may be periodically collected, such as every fewhours or every day, and added as metadata to the image in the imagedatabase or may added to a central repository or other branded imagedatabase.

The image identification and brand information processes may also gatherother information for association with the image or sensor data, such asthe volume of the container (if appropriate) or the UPC code of thewaste item (if visible). This information may help with auditing and mayalso have value for external purposes, including value in combinationwith branding information. All information gathered is tagged as imageinformation data and sent to the image database for association with theimage.

The master image database may then contain, for each waste item, thecaptured image, associated capture metadata (time and location), thecategorization for routing, and any image information data gathered(branding, logo, size, UPC, etc.).

In addition to on-site data collection for retraining, the neuralnetwork may also be trained via gathering of external data through amobile application running on camera-based device (phone, tablet, etc.).A user may point the device at any item and get predictions on what theneural network thinks it is seeing. The user may then identify anyerrors, and feed the correct data (either as original, oruser-corrected) into the system for retraining, hopefully avoiding andreducing on-site errors. The application can also be capable ofdownloading images that have been taken before for the purposes ofannotating the images on the device.

Information classified, labeled, and stored in databases, includinginformation gathered by bins 1000 and information gathered by mobileapplications, may be used in a variety of ways in addition to use intraining the neural network. This information may be used by bin 1000 toclassify and redirect items. This information may be used in audits,such as audits of diversion/recycling efficiency. This information mayalso be used by consolidated reporting systems reporting waste data,such as reporting waste data to a sustainability department.

In some embodiments, bin 1000 may also include exterior cameras or othersensors. Exterior cameras or sensors may be used to improve the securityof bin 1000 or to detect demographic information of persons depositingitems into bin 1000. A neural network, such as the image identificationneural network, the branding identification neural network, or anadditional network, may be used to detect demographic information suchas estimated age, ethnicity, and gender of persons depositing items intobin 1000. This demographic information could in some embodiments beassociated with branding information and provided to third parties,including providing the information to third parties for a fee or aspart of auditing information.

Systems or processes involving bin 1000 or multiple bins may be able toprovide real-time demographic and consumption information which may beof value, such as to those managing brands, auditing waste disposal,etc.

According to an embodiment, there is provided a waste bin with automateddetection and routing of waste items into appropriate receptacles.

According to an embodiment, there is provided a waste bin, comprising: acasing with an opening for inserting a waste item; two or morereceptacles disposed within the casing operative to receive waste items;a diverter disposed with the casing operative to direct the waste iteminto one of the receptacles; and an imaging system coupled to thediverter, the imaging system operative to image the waste item, performimage analysis on the image to determine an appropriate receptacle forthe waste item from the two or more receptacles, and set the diverter todirect the waste item into the appropriate receptacle.

In some embodiments, the imaging system further includes an ultrasonicsensor and a spectrometer.

In some embodiments, the imaging system further sends the image to astorage database.

In some embodiments, the image is provided with associated metadataprior to being sent.

In some embodiments, the metadata includes one or more of: time of imagecapture, location of image capture, and category of waste item.

According to an embodiment, there is provided a method of routing awaste item into a receptacle, comprising: imaging the waste item togenerate an image; analyzing the image using a neural network todetermine a category for the waste item; determining an appropriatereceptacle for the waste item based on the category; and sending asignal indicating the appropriate receptacle for the waste item.

In some embodiments, the method includes sending the image to a storagedatabase.

In some embodiments, the method includes attaching metadata to the imageprior to sending the image to the storage database.

In some embodiments, the metadata includes one or more of: time of imagecapture, location of image capture, and category of waste item.

The embodiments of the devices, systems and methods described herein maybe implemented in a combination of both hardware and software. Theseembodiments may be implemented on programmable computers, each computerincluding at least one processor, a data storage system (includingvolatile memory or non-volatile memory or other data storage elements ora combination thereof), and at least one communication interface.

Program code is applied to input data to perform the functions describedherein and to generate output information. The output information isapplied to one or more output devices. In some embodiments, thecommunication interface may be a network communication interface. Inembodiments in which elements may be combined, the communicationinterface may be a software communication interface, such as those forinter-process communication. In still other embodiments, there may be acombination of communication interfaces implemented as hardware,software, and combination thereof.

Throughout the foregoing discussion, numerous references will be maderegarding servers, services, interfaces, portals, platforms, or othersystems formed from computing devices. It should be appreciated that theuse of such terms is deemed to represent one or more computing deviceshaving at least one processor configured to execute softwareinstructions stored on a computer readable tangible, non-transitorymedium. For example, a server can include one or more computersoperating as a web server, database server, or other type of computerserver in a manner to fulfill described roles, responsibilities, orfunctions.

The technical solution of embodiments may be in the form of a softwareproduct. The software product may be stored in a non-volatile ornon-transitory storage medium, which can be a compact disk read-onlymemory (CD-ROM), a USB flash disk, or a removable hard disk. Thesoftware product includes a number of instructions that enable acomputer device (personal computer, server, or network device) toexecute the methods provided by the embodiments.

The embodiments described herein are implemented by physical computerhardware, including computing devices, servers, receivers, transmitters,processors, memory, displays, and networks. The embodiments describedherein provide useful physical machines and particularly configuredcomputer hardware arrangements.

Although the embodiments have been described in detail, it should beunderstood that various changes, substitutions and alterations can bemade herein.

Moreover, the scope of the present application is not intended to belimited to the particular embodiments of the process, machine,manufacture, composition of matter, means, methods and steps describedin the specification.

As can be understood, the examples described above and illustrated areintended to be exemplary only.

What is claimed is:
 1. A system for waste item detection comprising:sensors for detecting an approaching object to trigger a camera tocapture image data and one or more processors to process the image data;the camera for capturing image data of the approaching object;non-transitory computer readable storage medium with executableinstructions for causing the one or more processors to: process theimage data using a neural network to detect a waste item within theimage data and determine a category for the waste item, wherein theneural network defines classes for different waste items and maps thedetected waste item to the classes for the different waste items tocompute a pairing of the detected waste item and a class, the classbeing associated with the category for the waste item; and generatefeedback data indicating the category for the waste item.
 2. The systemof claim 1 wherein the one or more processors are configured to use theneural network estimate a location of a head of the object within theimage data and detect the waste item in the image data using theestimated location of the head within the image data, wherein the neuralnetwork is trained using image data to define data points for personfeatures, waste items, background and environment.
 3. The system ofclaim 1, wherein the one or more processors determine an appropriatereceptacle to dispose of the detected waste item, and wherein thefeedback data comprises an indication of the appropriate receptacle todispose of the detected waste item.
 4. The system of claim 2 wherein theone or more processors are configured to use the neural network todetect data points within the image data, the data points correspondingto a set of the person features, the set of the person features defininghead features for estimating the location of the head of the object. 5.The system of claim 2 wherein a portion of the data points correspondingto the background and the environment for filtering the image data tofocus on the detected waste item.
 6. The system of claim 2 wherein theone or more processors are configured to use the neural network estimatea location of hands within the image data based on the location of thehead and detect the waste item in the image data using the estimatedlocation of the hands within the image data.
 7. The system of claim 1wherein the one or more processors transmit the feedback data to adisplay device for displaying the feedback data.
 8. The system of claim1 wherein the neural network is trained through consecutive objectdetection to determine the appropriate receptacle that the waste itemshould be disposed.
 9. The system of claim 1 wherein the one or moreprocessors are configured to measure closeness of a user to theappropriate receptacle.
 10. The system of claim 1 wherein the sensorsdetect the approaching object by computing a continuous decrease insignal range for the approaching object.
 11. The system of claim 1wherein the camera or the sensors capture additional data indicatingdisposal of the waste item, wherein the one or more processors use theadditional data to determine whether the waste item was disposed in theappropriate receptacle, and generates additional feedback data based onthe determination
 12. The system of claim 3 wherein upon determiningthat the waste item was disposed in the appropriate receptacle, the oneor more processors generate a reward for redemption as the additionalfeedback.
 13. The system of claim 1 wherein the one or more processorsare configured to use the neural network estimate a location of handswithin the image data and detect the waste item in the image data usingthe estimated location of the hands within the image data.
 14. Thesystem of claim 13 wherein the neural network is trained using imagedata to define data points for person features, waste items, backgroundand environment.
 15. The system of claim 14 wherein the one or moreprocessors are configured to use the neural network to detect personfeatures within the image data, the person features defining handfeatures or head features for estimating the location of the hands. 17.The system of claim 14 wherein the one or more processors are configuredto use the neural network to detect regions of the image data asbackground and environment and filter the regions of the image data tofocus on the waste item in the image data.
 18. The system of claim 1wherein the one or more processors are configured to detect head datawithin the image data and blur the head data to generate sanitized imagedata.
 19. The system of claim 18 wherein the neural network is trainedusing image data to define data points for person features, wherein theperson features define head features for detecting the head data withinthe image data.
 20. The system of claim 1 wherein the one or moreprocessors are configured to tag the image data with metadata indicatinga system identifier, category for the waste item, location data, andtime data.
 21. The system of claim 1 further comprising a cloud serverconfigured to receive the image data tagged with metadata indicating thecategory for the waste item.
 22. The system of claim 21 wherein thecloud server is configured to process the image data to detect head datawithin the image data and blur the head data to generate sanitized imagedata.
 23. The system of claim 1 further comprising a cloud serverconfigured to receive the image data tagged with metadata indicating thecategory for the waste item, validate the image data, generate afirmware upgrade for the neural network, and transmit the firmwareupgrade to the one or more processors to update the neural network. 24.The system of claim 1 wherein the one or more processors are configuredto compute image analytic data including types of waste items, volume ofindividual waste item, monitored volume of each receptacle based on thewaste items disposed, and calculated diversion rate.
 25. A system forwaste item detection comprising: sensors for detecting an approachingobject to trigger image processing for detection and recognition; acamera for capturing image data of the approaching object;non-transitory computer readable storage medium with executableinstructions for causing one or more processors to: process the imagedata using a neural network to detect a waste item within the image dataand determine a category for the waste item; determine an appropriatereceptacle to dispose of the detected waste item; and generate feedbackdata indicating the category for the waste item; wherein the camera orthe sensors capture additional data indicating disposal of the wasteitem, wherein the one or more processors use the additional data todetermine whether the waste item was disposed in the appropriatereceptacle, and generates additional feedback data based on thedetermination.